{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "d07c3896",
   "metadata": {},
   "source": [
    "# Support Vector Machine (SVM) classification using Concrete ML\n",
    "\n",
    " In this tutorial, we show how to create, train, and evaluate a Support Vector Machine (SVM) model using Concrete ML library for a classification task.\n",
    "\n",
    "It is cut in 2 parts:\n",
    "1. a quick setup of a LinearSVC model with Concrete ML\n",
    "2. a more in-depth approach taking a closer look to the concrete-ml specifics\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "404fb028",
   "metadata": {},
   "source": [
    "## Introduction"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "d3654d52",
   "metadata": {},
   "source": [
    "### Concrete ML and useful links\n",
    "\n",
    "> Concrete ML is an open-source, privacy-preserving, machine learning inference framework based on fully homomorphic encryption (FHE). It enables data scientists without any prior knowledge of cryptography to automatically turn machine learning models into their FHE equivalent, using familiar APIs from Scikit-learn and PyTorch.\n",
    "> \n",
    "> <cite>&mdash; [Zama documentation](../README.md)</cite>\n",
    "\n",
    "This tutorial does not require any knowledge of Concrete ML. Newcomers might nonetheless be interested in reading some of the introductory sections of the official documentation, such as:\n",
    "\n",
    "- [What is Concrete ML](../README.md)\n",
    "- [Key Concepts](../getting-started/concepts.md)\n",
    "\n",
    "### Support Vector Machine\n",
    "\n",
    "SVM is a machine learning algorithm for classification and regression. LinearSVC is an efficient implementation of SVM\n",
    "that works best when the data is linearly separable. In this tutorial, we use the [pulsar star dataset](https://www.kaggle.com/datasets/colearninglounge/predicting-pulsar-starintermediate)  to determine whether a neutron star can be classified as a pulsar star.\n",
    "\n",
    "Concrete ML exposes a LinearSVC class which implements the\n",
    "[scikit-learn LinearSVC](https://scikit-learn.org/stable/modules/generated/sklearn.svm.LinearSVC.html) interface, so you should feel right at home.\n",
    "\n",
    "### Setup code\n",
    "\n",
    "Just as in any machine learning project, some libraries need to be imported."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "ea5f1461",
   "metadata": {},
   "outputs": [],
   "source": [
    "# display visualizations and plots in the notebook itself\n",
    "%matplotlib inline\n",
    "\n",
    "# import numpy and matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.metrics import accuracy_score, f1_score, make_scorer\n",
    "from sklearn.model_selection import GridSearchCV, train_test_split\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.svm import LinearSVC as SklearnLinearSVC\n",
    "\n",
    "# import the concrete-ml LinearSVC implementation\n",
    "from concrete.ml.sklearn.svm import LinearSVC as ConcreteLinearSVC"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "f17b4d03",
   "metadata": {},
   "source": [
    "The following code defines a visualization function that plots the decision boundary over the 2 principal components."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "125ef967",
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_decision_boundary(\n",
    "    clf,\n",
    "    X,\n",
    "    y,\n",
    "    title=\"LinearSVC Decision Boundary\",\n",
    "    xlabel=\"First Principal Component\",\n",
    "    ylabel=\"Second Principal Component\",\n",
    "):\n",
    "    # Perform PCA to reduce the dimensionality to 2\n",
    "    pca = PCA(n_components=2)\n",
    "    X_pca = pca.fit_transform(X)\n",
    "\n",
    "    # Create the mesh grid\n",
    "    x_min, x_max = X_pca[:, 0].min() - 1, X_pca[:, 0].max() + 1\n",
    "    y_min, y_max = X_pca[:, 1].min() - 1, X_pca[:, 1].max() + 1\n",
    "    xx, yy = np.meshgrid(np.arange(x_min, x_max, 0.02), np.arange(y_min, y_max, 0.02))\n",
    "\n",
    "    # Transform the mesh grid points back to the original feature space\n",
    "    mesh_points = pca.inverse_transform(np.c_[xx.ravel(), yy.ravel()])\n",
    "\n",
    "    # Make predictions using the classifier\n",
    "    Z = clf.predict(mesh_points)\n",
    "    Z = Z.reshape(xx.shape)\n",
    "\n",
    "    # Plot the decision boundary\n",
    "    _, ax = plt.subplots()\n",
    "    ax.contourf(xx, yy, Z, alpha=0.8)\n",
    "    ax.scatter(X_pca[:, 0], X_pca[:, 1], c=y, edgecolors=\"k\", marker=\"o\", s=50)\n",
    "\n",
    "    # Calculate the accuracy\n",
    "    accuracy = accuracy_score(y, clf.predict(X))\n",
    "\n",
    "    plt.xlabel(xlabel)\n",
    "    plt.ylabel(ylabel)\n",
    "    plt.title(f\"{title} (Accuracy: {accuracy:.4f})\")\n",
    "    plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "a9646f34",
   "metadata": {},
   "source": [
    "Import and preprocess the dataset and make some adjustments to it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "51ff667a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Mean of the integrated profile</th>\n",
       "      <th>Standard deviation of the integrated profile</th>\n",
       "      <th>Excess kurtosis of the integrated profile</th>\n",
       "      <th>Skewness of the integrated profile</th>\n",
       "      <th>Mean of the DM-SNR curve</th>\n",
       "      <th>Standard deviation of the DM-SNR curve</th>\n",
       "      <th>Excess kurtosis of the DM-SNR curve</th>\n",
       "      <th>Skewness of the DM-SNR curve</th>\n",
       "      <th>target_class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>121.156250</td>\n",
       "      <td>48.372971</td>\n",
       "      <td>0.375485</td>\n",
       "      <td>-0.013165</td>\n",
       "      <td>3.168896</td>\n",
       "      <td>18.399367</td>\n",
       "      <td>7.449874</td>\n",
       "      <td>65.159298</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>76.968750</td>\n",
       "      <td>36.175557</td>\n",
       "      <td>0.712898</td>\n",
       "      <td>3.388719</td>\n",
       "      <td>2.399666</td>\n",
       "      <td>17.570997</td>\n",
       "      <td>9.414652</td>\n",
       "      <td>102.722975</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>130.585938</td>\n",
       "      <td>53.229534</td>\n",
       "      <td>0.133408</td>\n",
       "      <td>-0.297242</td>\n",
       "      <td>2.743311</td>\n",
       "      <td>22.362553</td>\n",
       "      <td>8.508364</td>\n",
       "      <td>74.031324</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>156.398438</td>\n",
       "      <td>48.865942</td>\n",
       "      <td>-0.215989</td>\n",
       "      <td>-0.171294</td>\n",
       "      <td>17.471572</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.958066</td>\n",
       "      <td>7.197842</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>84.804688</td>\n",
       "      <td>36.117659</td>\n",
       "      <td>0.825013</td>\n",
       "      <td>3.274125</td>\n",
       "      <td>2.790134</td>\n",
       "      <td>20.618009</td>\n",
       "      <td>8.405008</td>\n",
       "      <td>76.291128</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Mean of the integrated profile  \\\n",
       "0                       121.156250   \n",
       "1                        76.968750   \n",
       "2                       130.585938   \n",
       "3                       156.398438   \n",
       "4                        84.804688   \n",
       "\n",
       "    Standard deviation of the integrated profile  \\\n",
       "0                                      48.372971   \n",
       "1                                      36.175557   \n",
       "2                                      53.229534   \n",
       "3                                      48.865942   \n",
       "4                                      36.117659   \n",
       "\n",
       "    Excess kurtosis of the integrated profile  \\\n",
       "0                                    0.375485   \n",
       "1                                    0.712898   \n",
       "2                                    0.133408   \n",
       "3                                   -0.215989   \n",
       "4                                    0.825013   \n",
       "\n",
       "    Skewness of the integrated profile   Mean of the DM-SNR curve  \\\n",
       "0                            -0.013165                   3.168896   \n",
       "1                             3.388719                   2.399666   \n",
       "2                            -0.297242                   2.743311   \n",
       "3                            -0.171294                  17.471572   \n",
       "4                             3.274125                   2.790134   \n",
       "\n",
       "    Standard deviation of the DM-SNR curve  \\\n",
       "0                                18.399367   \n",
       "1                                17.570997   \n",
       "2                                22.362553   \n",
       "3                                      NaN   \n",
       "4                                20.618009   \n",
       "\n",
       "    Excess kurtosis of the DM-SNR curve   Skewness of the DM-SNR curve  \\\n",
       "0                              7.449874                      65.159298   \n",
       "1                              9.414652                     102.722975   \n",
       "2                              8.508364                      74.031324   \n",
       "3                              2.958066                       7.197842   \n",
       "4                              8.405008                      76.291128   \n",
       "\n",
       "   target_class  \n",
       "0           0.0  \n",
       "1           0.0  \n",
       "2           0.0  \n",
       "3           0.0  \n",
       "4           0.0  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get the data\n",
    "df = pd.read_csv(\n",
    "    \"https://gist.githubusercontent.com/robinstraub/72f1cb27829dba85f49f68210979f561/\"\n",
    "    \"raw/b9982ae654967028f6f4010bd235d850d38fe25b/pulsar-star-dataset.csv\"\n",
    ")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "074883c5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Extract the features and labels\n",
    "X = df.drop(columns=[\"target_class\"])\n",
    "y = df[\"target_class\"]\n",
    "\n",
    "# Replace N/A values with the mean of the respective feature\n",
    "X.fillna(X.mean(), inplace=True)\n",
    "\n",
    "# Split the data into train and test sets\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
    "\n",
    "# Scale the data\n",
    "scaler = StandardScaler()\n",
    "X_train = scaler.fit_transform(X_train)\n",
    "X_test = scaler.transform(X_test)\n",
    "\n",
    "# Convert the floating labels to integer labels for both train and test sets\n",
    "y_train = y_train.astype(int)\n",
    "y_test = y_test.astype(int)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "12e827d0",
   "metadata": {},
   "source": [
    "## Part 1: Train a simple model with Concrete ML\n",
    "\n",
    "The following code quickly scaffolds a Concrete ML LinearSVC code, which should sound familiar.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "38613861",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Train a model with scikit-learn LinearSVC, perform prediction and compute the accuracy\n",
    "svm_sklearn = SklearnLinearSVC(max_iter=100)\n",
    "svm_sklearn.fit(X_train, y_train)\n",
    "# plot the boundary\n",
    "plot_decision_boundary(svm_sklearn, X_test, y_test)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "78a757a2",
   "metadata": {},
   "source": [
    "Hopefully should not be confused by this, otherwise you may want to read the [official scikit-learn SVM documentation](https://scikit-learn.org/stable/modules/svm.html#svm-classification). \n",
    "\n",
    "The algorithm can be tweaked with the parameters exposed by the LinearSVC class, refer to the  [LinearSVC API documentation](https://scikit-learn.org/stable/modules/generated/sklearn.svm.LinearSVC.html) as to what can be customized. \n",
    "\n",
    "2 main components of the dataset are plotted, however do note that the latter has 8 features so this is a simplified representation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "b2004595",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Perform the same steps with the Concrete ML LinearSVC implementation\n",
    "svm_concrete = ConcreteLinearSVC(max_iter=100, n_bits=8)\n",
    "svm_concrete.fit(X_train, y_train)\n",
    "# plot the boundary\n",
    "plot_decision_boundary(svm_concrete, X_test, y_test)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "05104312",
   "metadata": {},
   "source": [
    "The decision boundary is not a straight line anymore and appears \"noisy\", which is caused by the data being quantized. The prediction remains accurate nonetheless. Here the prediction is equal to the scikit-learn implementation, however reducing the bit size can induce a drop in accuracy, as the quantization reduces the data precision.\n",
    "\n",
    "One thing to note here is not only the model is trained using clear data, but the prediction are also performed in a *plain environment*, there is no encryption at this stage.\n",
    "\n",
    "In order to perform prediction in an FHE environment, the model first has to be compiled into a circuit."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "a854f839",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "FHE Accuracy: 0.9765 (bit-width: 17)\n"
     ]
    }
   ],
   "source": [
    "# A circuit needs to be compiled to enable FHE execution\n",
    "circuit = svm_concrete.compile(X_train)\n",
    "# Now that a circuit is compiled, the svm_concrete can predict value with FHE\n",
    "y_pred = svm_concrete.predict(X_test, fhe=\"execute\")\n",
    "accuracy = accuracy_score(y_test, y_pred)\n",
    "# print the accuracy\n",
    "print(f\"FHE Accuracy: {accuracy:.4f} (bit-width: {circuit.graph.maximum_integer_bit_width()})\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "9a4de0e7",
   "metadata": {},
   "source": [
    "Now that the model is compiled, computing predictions with FHE is just a matter of passing a `fhe` parameter to `execute`.\n",
    "\n",
    "Note that the accuracy of the predictions over FHE are the same as over plain (quantized) data -- FHE execution does not alter the model accuracy."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "51afbfe2",
   "metadata": {},
   "source": [
    "### Key takeaways\n",
    "\n",
    "#### Simplicity of execution\n",
    "\n",
    "For a high-level use-case, Concrete ML offers a very similar interface to scikit-learn. The main difference is *a model needs to be compiled to allow execution in FHE*.\n",
    "\n",
    "#### Model Accuracy\n",
    "\n",
    "Concrete ML prediction accuracy can be slightly worse than a regular scikit-learn implementation. This is because of [quantization](../explanations/quantization.md): number precision needs to be fixed-size for the model to be evaluated in FHE. This can be alleviated down to where the accuracy difference is none or negligible (which is the case here with a 8 bit size).\n",
    "\n",
    "#### Execution time\n",
    "\n",
    "The execution speed can be slower in Concrete ML, especially during compilation and FHE inference phases, because enabling FHE operations uses more resources than regular inference on plain data. However, the speed can be improved by decreasing the precision of the data and model's weights thanks to the n_bits parameter. But, depending on the project, there is a trade-off between a slower but more accurate model and a faster but less accurate model."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "cdc4c4d2",
   "metadata": {},
   "source": [
    "## Part 2: In-Depth model development\n",
    "\n",
    "This is on a more in-depth approach, showing how to effectively develop with concrete-ml. This follows the steps of [model development](../getting-started/concepts.md#i.-model-development)\n",
    "\n",
    "Especially:\n",
    "- the effects of quantization and finding the good bit number\n",
    "- setup the fhe simulation to speed up the development workflow\n",
    "- use inference to use the model with encrypted data\n",
    "\n",
    "---\n",
    "\n",
    "### Step a: training the model\n",
    "\n",
    "Nothing new under the sun here, a relevant model needs to be trained for this machine-learning problem. We can just take back what we used so far."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "80f701f3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Scikit-learn Accuracy: 0.9769\n"
     ]
    }
   ],
   "source": [
    "# setup and train a scikit-learn LinearSVC model, just as before\n",
    "svm_sklearn = SklearnLinearSVC()\n",
    "svm_sklearn.fit(X_train, y_train)\n",
    "# predict some test data and measure the model accuracy\n",
    "y_pred_sklearn = svm_sklearn.predict(X_test)\n",
    "accuracy_sklearn = accuracy_score(y_test, y_pred_sklearn)\n",
    "\n",
    "print(f\"Scikit-learn Accuracy: {accuracy_sklearn:.4f}\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "ce2920d8",
   "metadata": {},
   "source": [
    "Not too shabby.\n",
    "\n",
    "### Step b: quantize the model\n",
    "\n",
    "So far most of Concrete ML specificities have conveniently been avoided for the sake of simplicity. The first Concrete ML specific step of developping a model is to quantize it, which soberly means to turn the model into an integer equivalent.\n",
    "\n",
    "Although it is strongly encouraged to read the [Zama introduction to quantization](../explanations/quantization.md), the key takeaway is **a model needs to be reduced to a *discrete*, smaller set in order for the encryption to happen**. Otherwise the data becomes too large to be manipulated in FHE. \n",
    "\n",
    "As of v1.0 the maximum bit size is 16. The lighter the bit size the more efficient the concrete-ml model is. Thus the goal of the quantization step is to find the lowest bit size value that offers an acceptable accuracy, so the model efficiency is maximized."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "e11c0952",
   "metadata": {},
   "source": [
    "We can see that the accuracy increase with the bit size, although it remains quite good even for lower bit sizes.\n",
    "\n",
    "Let's work on a more objective way of selecting the best n_bit parameter alongside some common parameters for LinearSVC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "d660eceb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_5fafd_row1_col0, #T_5fafd_row1_col1, #T_5fafd_row1_col2, #T_5fafd_row1_col3, #T_5fafd_row1_col4 {\n",
       "  background-color: green;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_5fafd\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th id=\"T_5fafd_level0_col0\" class=\"col_heading level0 col0\" >param_n_bits</th>\n",
       "      <th id=\"T_5fafd_level0_col1\" class=\"col_heading level0 col1\" >param_C</th>\n",
       "      <th id=\"T_5fafd_level0_col2\" class=\"col_heading level0 col2\" >param_penalty</th>\n",
       "      <th id=\"T_5fafd_level0_col3\" class=\"col_heading level0 col3\" >param_dual</th>\n",
       "      <th id=\"T_5fafd_level0_col4\" class=\"col_heading level0 col4\" >mean_test_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td id=\"T_5fafd_row0_col0\" class=\"data row0 col0\" >2</td>\n",
       "      <td id=\"T_5fafd_row0_col1\" class=\"data row0 col1\" >0.001000</td>\n",
       "      <td id=\"T_5fafd_row0_col2\" class=\"data row0 col2\" >l2</td>\n",
       "      <td id=\"T_5fafd_row0_col3\" class=\"data row0 col3\" >False</td>\n",
       "      <td id=\"T_5fafd_row0_col4\" class=\"data row0 col4\" >0.945603</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_5fafd_row1_col0\" class=\"data row1 col0\" >3</td>\n",
       "      <td id=\"T_5fafd_row1_col1\" class=\"data row1 col1\" >1.000000</td>\n",
       "      <td id=\"T_5fafd_row1_col2\" class=\"data row1 col2\" >l1</td>\n",
       "      <td id=\"T_5fafd_row1_col3\" class=\"data row1 col3\" >False</td>\n",
       "      <td id=\"T_5fafd_row1_col4\" class=\"data row1 col4\" >0.971102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_5fafd_row2_col0\" class=\"data row2 col0\" >4</td>\n",
       "      <td id=\"T_5fafd_row2_col1\" class=\"data row2 col1\" >0.010000</td>\n",
       "      <td id=\"T_5fafd_row2_col2\" class=\"data row2 col2\" >l2</td>\n",
       "      <td id=\"T_5fafd_row2_col3\" class=\"data row2 col3\" >False</td>\n",
       "      <td id=\"T_5fafd_row2_col4\" class=\"data row2 col4\" >0.970423</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_5fafd_row3_col0\" class=\"data row3 col0\" >5</td>\n",
       "      <td id=\"T_5fafd_row3_col1\" class=\"data row3 col1\" >10.000000</td>\n",
       "      <td id=\"T_5fafd_row3_col2\" class=\"data row3 col2\" >l1</td>\n",
       "      <td id=\"T_5fafd_row3_col3\" class=\"data row3 col3\" >False</td>\n",
       "      <td id=\"T_5fafd_row3_col4\" class=\"data row3 col4\" >0.973795</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_5fafd_row4_col0\" class=\"data row4 col0\" >6</td>\n",
       "      <td id=\"T_5fafd_row4_col1\" class=\"data row4 col1\" >10.000000</td>\n",
       "      <td id=\"T_5fafd_row4_col2\" class=\"data row4 col2\" >l2</td>\n",
       "      <td id=\"T_5fafd_row4_col3\" class=\"data row4 col3\" >True</td>\n",
       "      <td id=\"T_5fafd_row4_col4\" class=\"data row4 col4\" >0.973072</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_5fafd_row5_col0\" class=\"data row5 col0\" >7</td>\n",
       "      <td id=\"T_5fafd_row5_col1\" class=\"data row5 col1\" >1.000000</td>\n",
       "      <td id=\"T_5fafd_row5_col2\" class=\"data row5 col2\" >l1</td>\n",
       "      <td id=\"T_5fafd_row5_col3\" class=\"data row5 col3\" >False</td>\n",
       "      <td id=\"T_5fafd_row5_col4\" class=\"data row5 col4\" >0.972945</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_5fafd_row6_col0\" class=\"data row6 col0\" >8</td>\n",
       "      <td id=\"T_5fafd_row6_col1\" class=\"data row6 col1\" >10.000000</td>\n",
       "      <td id=\"T_5fafd_row6_col2\" class=\"data row6 col2\" >l2</td>\n",
       "      <td id=\"T_5fafd_row6_col3\" class=\"data row6 col3\" >True</td>\n",
       "      <td id=\"T_5fafd_row6_col4\" class=\"data row6 col4\" >0.973254</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_5fafd_row7_col0\" class=\"data row7 col0\" >9</td>\n",
       "      <td id=\"T_5fafd_row7_col1\" class=\"data row7 col1\" >0.100000</td>\n",
       "      <td id=\"T_5fafd_row7_col2\" class=\"data row7 col2\" >l2</td>\n",
       "      <td id=\"T_5fafd_row7_col3\" class=\"data row7 col3\" >False</td>\n",
       "      <td id=\"T_5fafd_row7_col4\" class=\"data row7 col4\" >0.972720</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_5fafd_row8_col0\" class=\"data row8 col0\" >10</td>\n",
       "      <td id=\"T_5fafd_row8_col1\" class=\"data row8 col1\" >10.000000</td>\n",
       "      <td id=\"T_5fafd_row8_col2\" class=\"data row8 col2\" >l2</td>\n",
       "      <td id=\"T_5fafd_row8_col3\" class=\"data row8 col3\" >True</td>\n",
       "      <td id=\"T_5fafd_row8_col4\" class=\"data row8 col4\" >0.973402</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_5fafd_row9_col0\" class=\"data row9 col0\" >11</td>\n",
       "      <td id=\"T_5fafd_row9_col1\" class=\"data row9 col1\" >100.000000</td>\n",
       "      <td id=\"T_5fafd_row9_col2\" class=\"data row9 col2\" >l2</td>\n",
       "      <td id=\"T_5fafd_row9_col3\" class=\"data row9 col3\" >True</td>\n",
       "      <td id=\"T_5fafd_row9_col4\" class=\"data row9 col4\" >0.973878</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_5fafd_row10_col0\" class=\"data row10 col0\" >12</td>\n",
       "      <td id=\"T_5fafd_row10_col1\" class=\"data row10 col1\" >1.000000</td>\n",
       "      <td id=\"T_5fafd_row10_col2\" class=\"data row10 col2\" >l2</td>\n",
       "      <td id=\"T_5fafd_row10_col3\" class=\"data row10 col3\" >False</td>\n",
       "      <td id=\"T_5fafd_row10_col4\" class=\"data row10 col4\" >0.972929</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_5fafd_row11_col0\" class=\"data row11 col0\" >13</td>\n",
       "      <td id=\"T_5fafd_row11_col1\" class=\"data row11 col1\" >100.000000</td>\n",
       "      <td id=\"T_5fafd_row11_col2\" class=\"data row11 col2\" >l2</td>\n",
       "      <td id=\"T_5fafd_row11_col3\" class=\"data row11 col3\" >True</td>\n",
       "      <td id=\"T_5fafd_row11_col4\" class=\"data row11 col4\" >0.973529</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_5fafd_row12_col0\" class=\"data row12 col0\" >14</td>\n",
       "      <td id=\"T_5fafd_row12_col1\" class=\"data row12 col1\" >1.000000</td>\n",
       "      <td id=\"T_5fafd_row12_col2\" class=\"data row12 col2\" >l1</td>\n",
       "      <td id=\"T_5fafd_row12_col3\" class=\"data row12 col3\" >False</td>\n",
       "      <td id=\"T_5fafd_row12_col4\" class=\"data row12 col4\" >0.972817</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_5fafd_row13_col0\" class=\"data row13 col0\" >15</td>\n",
       "      <td id=\"T_5fafd_row13_col1\" class=\"data row13 col1\" >100.000000</td>\n",
       "      <td id=\"T_5fafd_row13_col2\" class=\"data row13 col2\" >l2</td>\n",
       "      <td id=\"T_5fafd_row13_col3\" class=\"data row13 col3\" >True</td>\n",
       "      <td id=\"T_5fafd_row13_col4\" class=\"data row13 col4\" >0.973064</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_5fafd_row14_col0\" class=\"data row14 col0\" >16</td>\n",
       "      <td id=\"T_5fafd_row14_col1\" class=\"data row14 col1\" >10.000000</td>\n",
       "      <td id=\"T_5fafd_row14_col2\" class=\"data row14 col2\" >l2</td>\n",
       "      <td id=\"T_5fafd_row14_col3\" class=\"data row14 col3\" >True</td>\n",
       "      <td id=\"T_5fafd_row14_col4\" class=\"data row14 col4\" >0.972988</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7f1e2bbea580>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svm = ConcreteLinearSVC()\n",
    "\n",
    "# Define the parameter grid for the grid search\n",
    "param_grid = param_grid = [\n",
    "    {\n",
    "        \"C\": np.logspace(-3, 3, 7),\n",
    "        \"n_bits\": range(2, 17),\n",
    "        \"penalty\": [\"l1\", \"l2\"],\n",
    "        \"dual\": [False, True],\n",
    "    },\n",
    "]\n",
    "\n",
    "# Use the F1 score as the metric to optimize, as it provides a\n",
    "# balanced trade-off between precision and recall\n",
    "scorer = make_scorer(f1_score, average=\"weighted\")\n",
    "\n",
    "# Set up the grid search with the custom scoring function\n",
    "grid_search = GridSearchCV(svm, param_grid, scoring=scorer, cv=5, n_jobs=1)\n",
    "\n",
    "# Fit the grid search to the data\n",
    "grid_search.fit(X_train, y_train)\n",
    "\n",
    "# Convert the grid search results into a pandas DataFrame\n",
    "results_df = pd.DataFrame(grid_search.cv_results_)\n",
    "\n",
    "# Define a custom function to highlight a specific row based on n_bits value\n",
    "\n",
    "\n",
    "def highlight_row(row, n_bits_value=3, color=\"green\"):\n",
    "    return [\n",
    "        f\"background-color: {color}\" if row[\"param_n_bits\"] == n_bits_value else \"\" for _ in row\n",
    "    ]\n",
    "\n",
    "\n",
    "# Find the best hyperparameter combination for each n_bits value\n",
    "best_results = results_df.loc[results_df.groupby(\"param_n_bits\")[\"mean_test_score\"].idxmax()]\n",
    "best_results = best_results[\n",
    "    [\"param_n_bits\", \"param_C\", \"param_penalty\", \"param_dual\", \"mean_test_score\"]\n",
    "]\n",
    "best_results.reset_index(drop=True, inplace=True)\n",
    "\n",
    "# Display the best results DataFrame\n",
    "best_results.style.apply(highlight_row, n_bits_value=3, axis=1).hide()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "4751c145",
   "metadata": {},
   "source": [
    "Here, n_bits = 3 is the best compromise between latency and performance."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "122bd347",
   "metadata": {},
   "source": [
    "### Step c: simulate the model execution\n",
    "\n",
    "Executing models with FHE can prove to be a slow process, depending on:\n",
    "- the data-set size\n",
    "- the model itself\n",
    "- the hardware executing the model\n",
    "\n",
    "Setting up a model in Concrete ML requires some additional work compared to standard models. For instance, users must select the quantization bit-width for both the model's weight and input data, which can be complex and time-consuming while using real FHE inference. However, Concrete ML provides an FHE simulation mode that allows users to identify optimal hyper-parameters with the best trade-off between latency and performance.\n",
    "\n",
    "> Testing FHE models on very large data-sets can take a long time. Furthermore, not all models are compatible with FHE constraints out-of-the-box. Simulation using the FHE simulation allows you to execute a model that was quantized, to measure the accuracy it would have in FHE, but also to determine the modifications required to make it FHE compatible.\n",
    ">\n",
    "> — [Zama documentation](../getting-started/concepts.md#i.-model-development)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "74f5bf79",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy with FHE simulation: 0.9745\n"
     ]
    }
   ],
   "source": [
    "svm_concrete = ConcreteLinearSVC(n_bits=3, C=1, dual=False, penalty=\"l1\")\n",
    "svm_concrete.fit(X_train, y_train)\n",
    "\n",
    "# compile the model\n",
    "circuit = svm_concrete.compile(X_train)\n",
    "\n",
    "# the model can now be executed with FHE\n",
    "y_pred = svm_concrete.predict(X_test, fhe=\"simulate\")\n",
    "accuracy = accuracy_score(y_test, y_pred)\n",
    "print(f\"Accuracy with FHE simulation: {accuracy:.4f}\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "e1721c59",
   "metadata": {},
   "source": [
    "*The FHE simulation enables some unsafe features, so it should understandably not be used for production*\n",
    "\n",
    "So far so good, the model is compiled and executed much quicker with the virtual library, allowing us to run it more efficiently in development environments."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "0f3bb9e8",
   "metadata": {},
   "source": [
    "### Step d: compile the model\n",
    "\n",
    "Now that we have selected a relevant bit size and parameters we can compile the model, with FHE execution instead of FHE simulation, so we can use it in production"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "861502da",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy with FHE execution: 0.9745\n"
     ]
    }
   ],
   "source": [
    "# predict the test set to verify the compiled model accuracy\n",
    "y_pred = svm_concrete.predict(X_test, fhe=\"execute\")\n",
    "accuracy = accuracy_score(y_test, y_pred)\n",
    "print(f\"Accuracy with FHE execution: {accuracy:.4f}\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "00a2461e",
   "metadata": {},
   "source": [
    "## Conclusion\n",
    "\n",
    "Setting up FHE with Concrete ML on a LinearSVC model is very simple, in the regard that Concrete ML provides an implementation of the [scikit-learn LinearSVC interface](https://scikit-learn.org/stable/modules/generated/sklearn.svm.LinearSVC.html). As a matter of fact, a working FHE model can be setup with just a few lines of code.\n",
    "\n",
    "Setting up a model with FHE benefits nonetheless from some additional work. For LinearSVC models, the main point is to select a relevant bit-size for [quantizing](../explanations/quantization.md) the model. Some additional tools can smooth up the development workflow, such as alleviating the [compilation](../explanations/compilation.md) time by making use of the [FHE simulation](../explanations/compilation.md#fhe-simulation) \n",
    "\n",
    "Once the model is carefully trained and quantized, it is ready to be deployed and used in production. Here are some useful links that cover this subject:\n",
    "- [Inference in the Cloud](../getting-started/cloud.md) summarize the steps for cloud deployment\n",
    "- [Production Deployment](../guides/client_server.md) offers a high-level view of how to deploy a Concrete ML model in a client/server setting.\n",
    "- [Client Server in Concrete ML](ClientServer.ipynb) provides a more hands-on approach as another tutorial."
   ]
  }
 ],
 "metadata": {
  "execution": {
   "timeout": 10800
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
